Forecasting for Smart Grid Applications with Higher Order Neural Networks

被引：0

作者：

Ricalde, Luis J. ^{[1
]}

Cruz, Braulio ^{[1
]}

Catzin, Glendy ^{[1
]}

Alanis, Alma Y. ^{[2
]}

Sanchez, Edgar N. ^{[2
]}

机构：

[1] Univ Autonoma Yucatan, Sch Engn, Merida, Yucatan, Mexico

[2] CUCEI, SINVESTAV, Guadalajara, Jalisco, Mexico

来源：

2012 WORLD AUTOMATION CONGRESS (WAC) | 2012年

关键词：

Higher Order Neural Network; Kalman filtering; Time series forecasting; Wind Energy; Smart Grid;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This work presents the design of a neural network which combines higher order terms in its input layer and an Extended Kalman Filter (EKF) based algorithm for its training. The neural network based scheme is defined as a Higher Order Neural Network (HONN) and its applicability is illustrated by means of time series forecasting for three important variables present in smart grids: Electric Load Demand (ELD), Wind Speed (WS) and Wind Energy Generation (WEG). The proposed model is trained and tested using real data values taken from a microgrid system in the UADY School of Engineering. The length of the regression vector is determined via the Lipschitz quotients methodology.

引用

页数：6

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